US10002154B1 - Computer data system data source having an update propagation graph with feedback cyclicality - Google Patents
Computer data system data source having an update propagation graph with feedback cyclicality Download PDFInfo
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Definitions
- Embodiments relate generally to computer data systems, and more particularly, to methods, systems and computer readable media for data source refreshing using an update propagation graph with feedback cyclicality.
- Data sources or objects within a computer data system may include static sources and dynamic sources. Some data sources or objects (e.g., tables) may depend on other data sources. As new data is received or obtained for dynamic data sources, those dynamic data sources may be refreshed (or updated). Data sources or objects that are dependent on one or more dynamic sources that have been refreshed may also need to be refreshed. The refreshing of data sources may need to be performed in an order based on dependencies.
- the dependencies may be defined by an update propagation graph. A need may exist to provide a feedback mechanism within the update propagation graph for purposes such as backtesting of a computer data system or of a data model or technique associated with the computer data system.
- Some implementations can include a system for updating a data object using an update propagation graph having a cyclicality feedback provider.
- the system can include one or more hardware processors coupled to a nontransitory computer readable medium having stored thereon software instructions that, when executed by the one or more processors, cause the one or more processors to perform operations.
- the operations can include constructing a cyclicality feedback provider including a cyclicality feedback provider object including one or more feedback data fields, and obtaining a reference to the cyclicality feedback provider object.
- the operations can also include constructing a computer data system update propagation graph having one or more update propagation graph data fields that correspond to the one or more feedback data fields.
- the operations can further include adding a feedback provider listener to the computer data system update propagation graph, wherein the feedback provider listener provides feedback updates to the one or more feedback data fields of the cyclicality feedback provider object when changes to the one or more update propagation graph data fields corresponding to the one or more feedback data fields are detected, and wherein the feedback updates are provided to the one or more feedback data fields of the cyclicality feedback provider object based on a state of a logical clock and on completion of update processing for a given logical clock cycle.
- the cyclicality feedback provider object can include a computer data system table object.
- the update propagation graph can include a hybrid directed acyclic graph having a clock-state controlled cyclicality feedback provided by the cyclicality feedback provider and a state of a logical clock.
- the operations can also include determining that a logical clock has transitioned to an update state, and processing events and updates to data sources of the update propagation graph for a current logical clock cycle, wherein processing the events and updates are performed on the hybrid directed acyclic graph as if the cyclicality feedback is not present.
- the operations can further include after the processing events and updates has completed, providing events and updates from the cyclicality feedback provider object to one or more data objects within the update propagation graph, wherein the events and updates from the cyclicality feedback provider object will be processed through the update propagation graph in a next logical clock cycle.
- the operations can also include performing a backtesting operation by providing predetermined input data to the update propagation graph as one or more events and updates to one or more data sources, and receiving output results from the update propagation graph for each logical clock cycle.
- the operations can further include comparing the output results received from the update propagation graph with one or more reference values, and generating an output signal based on the comparing.
- the method can also include determining that a logical clock has transitioned to an update state, and processing events and updates to data sources of the update propagation graph for a current logical clock cycle, wherein processing the events and updates are performed on the hybrid directed acyclic graph as if the cyclicality feedback is not present.
- the method can further include after the processing events and updates has completed, providing events and updates from the cyclicality feedback provider object to one or more data objects within the update propagation graph, wherein the events and updates from the cyclicality feedback provider object will be processed through the update propagation graph in a next logical clock cycle.
- the method can also include performing a backtesting operation by providing predetermined input data to the update propagation graph as one or more events and updates to one or more data sources, and receiving output results from the update propagation graph for each logical clock cycle.
- the method can further include comparing the output results received from the update propagation graph with one or more reference values, and generating an output signal based on the comparing.
- the operations can further include, after the processing events and updates has completed, providing events and updates from the cyclicality feedback provider object to one or more data objects within the update propagation graph, wherein the events and updates from the cyclicality feedback provider object will be processed through the update propagation graph in a next logical clock cycle.
- FIG. 1 is a diagram of an example computer data system and network 100 showing an example data distribution configuration in accordance with some implementations.
- the system 100 includes an application host 102 , a periodic data import host 104 , a query server host 106 , a long-term file server 108 , and a user data import host 110 .
- tables are used as an example data object in the description below, it will be appreciated that the data system described herein can also process other data objects such as mathematical objects (e.g., a singular value decomposition of values in a given range of one or more rows and columns of a table), TableMap objects, etc.
- a TableMap object provides the ability to lookup a Table by some key.
- a data log tailer 116 can be configured to access the sequential, row-oriented log file(s) 114 to retrieve input data logged by the data logging process.
- the data log tailer 116 can be configured to perform strict byte reading and transmission (e.g., to the data import server 120 ).
- the data import server 120 can be configured to store the input data into one or more corresponding data stores such as the periodic table data store 122 in a column-oriented configuration.
- the periodic table data store 122 can be used to store data that is being received within a time period (e.g., a minute, an hour, a day, etc.) and which may be later processed and stored in a data store of the long-term file server 108 .
- the production client host 202 can include a batch query application 212 (e.g., a query that is executed from a command line interface or the like) and a real time query data consumer process 214 (e.g., an application that connects to and listens to tables created from the execution of a separate query).
- the batch query application 212 and the real time query data consumer 214 can connect to a remote query dispatcher 222 and one or more remote query processors ( 224 , 226 ) within the query server host 1 208 .
- the DAG When query code is executed, the DAG is created or modified. As part of this process, the system records the order in which the DAG nodes were constructed in. This “construction ordering” can be used to determine the order that nodes are processed in the DAG.
- FIGS. 5A and 5B show example data source definitions and a corresponding hybrid DAG having a cyclicality feedback provider in accordance with some implementations.
- example code defines the data sources as tables (t 1 -t 5 ), where table t 1 includes data from the cyclicality feedback provider that provides a feedback path within the hybrid DAG for data from table t 3 .
- a hybrid DAG can be generated as shown by the graph in FIG. 5B .
- the hybrid DAG in FIG. 5B shows dependencies between the nodes, which correspond to table data sources, and also shows the cyclicality feedback provider that provides feedback data from table t 3 to table t 1 .
- the cyclicality feedback provider can be configured to listen to one or more tables (or other data sources). In addition to listening (or monitoring) for changes in data sources, a cyclicality feedback provider listener can listen for events such as state changes, state updates or other changes in addition to or as an alternative to listening for AMDR-type changes or events.
- FIG. 6 is a flowchart of an example cyclicality feedback provider method 600 in accordance with some implementations.
- Processing begins at 602 , where a new cyclicality feedback provider is created.
- the example pseudo code of FIG. 5A shows example pseudo code lines (e.g., first three lines of FIG. 5A ) that create a new cyclicality feedback provider.
- Creation of a cyclicality feedback provider can include instantiating an object of a class that has been configured to operate as a cyclicality feedback provider object.
- Processing continues to 604 .
- a reference to the cyclicality feedback provider object is obtained.
- a reference to a table of the cyclicality feedback provider can be requested as illustrated in line four of the pseudo code in FIG. 5A . Processing continues to 606 .
- an update propagation graph is constructed. For example, using techniques described above, an update propagation graph (e.g., as represented by FIG. 5B ) is constructed. Processing continues to 608 .
- the cyclicality feedback provider is configured to listen to changes (e.g., events and/or updates) of the data sources to which the cyclicality feedback provider is associated (e.g., the data sources or tables having data fields that the cyclicality feedback provider is configured to listen for events or changes to occur. Processing continues to 610 .
- events and/or updates for the data sources (e.g., tables) within the update propagation graph are processed according to the techniques mentioned above. Processing continues to 612 .
- the cyclicality feedback provider listener listens for changes. For example, the listener could programmatically detect a change in one or more data sources or data fields that the cyclicality feedback provider is listening to. Processing continues to 614 .
- processing continues to 616 . If no changes were detected, processing continues to 610 .
- the cyclicality feedback provider data object (e.g., table) is updated once the events and/or changes for the update propagation graph have been processed for the current logical clock cycle. For example, data changes in data sources monitored by the cyclicality feedback provider can be reflected in the cyclicality feedback provider data object. Processing continues to 618 .
- data sources that depend on the cyclicality feedback provider are updated at the end of the current logical cycle so that the feedback data is available for processing during the next logical clock cycle.
- 602 - 618 can be repeated in whole or in part to perform a cyclicality feedback operation.
- FIG. 7 is a flowchart of an example data source refresh process 700 using a hybrid DAG having a cyclicality feedback provider in accordance with some implementations.
- Processing begins at 702 , where it is determined that a logical clock has transitioned to an update state.
- the transition to an update state can mark the beginning of a logical clock cycle for updating an update propagation graph as discussed above.
- Processing continues to 704 .
- events and updates from the cyclicality feedback provider are provided to the data sources that are dependent on the cyclicality feedback provider. Processing continues to 708 .
- FIG. 8 is a diagram of an example computer data system 802 and backtesting application 804 using a hybrid DAG having a cyclicality feedback provider 806 in accordance with some implementations.
- the backtesting application 804 can provide predetermined input data 808 to the update propagation graph of the computer data system 802 .
- the cyclicality feedback provider 806 can provide feedback within the update propagation graph.
- Output 810 from the computer data system 802 can be received by the backtesting application/system 804 and programmatically evaluated to generate an output signal (e.g., new/modified input data 808 , and/or an indication of how the computer data system 802 is performing based on the input data 808 ).
- the backtesting application/system 804 can be part of the computer data system 802 , or can be a separate system.
- the cyclicality feedback provider 806 can be part of the backtesting application/system 804 or part of the computer data system 802 , or distributed between the two.
- FIG. 9 is a diagram of an example machine learning system 906 using a hybrid DAG having a cyclicality feedback provider in accordance with some implementations.
- data from data sources 1 and 2 ( 902 and 904 ) is provided to the machine learning model or system 906 , which processes the input data according to a machine learning technique (e.g., neural network, etc.).
- the machine learning model or system 906 generates a prediction 908 (or other inference or estimation).
- a cyclicality feedback provider 910 listens to changes in the machine learning model or system 906 can provides updates to the machine learning model or system 906 as described above.
- the instructions can be compiled from source code instructions provided in accordance with a programming language such as Java, C, C++, C#.net, assembly or the like.
- the instructions can also comprise code and data objects provided in accordance with, for example, the Visual BasicTM language, a specialized database query language, or another structured or object-oriented programming language.
- the sequence of programmed instructions, or programmable logic device configuration software, and data associated therewith can be stored in a nontransitory computer-readable medium such as a computer memory or storage device which may be any suitable memory apparatus, such as, but not limited to ROM, PROM, EEPROM, RAM, flash memory, disk drive and the like.
- the modules, processors or systems described above can be implemented as a programmed general purpose computer, an electronic device programmed with microcode, a hard-wired analog logic circuit, software stored on a computer-readable medium or signal, an optical computing device, a networked system of electronic and/or optical devices, a special purpose computing device, an integrated circuit device, a semiconductor chip, and/or a software module or object stored on a computer-readable medium or signal, for example.
- embodiments of the disclosed method, system, and computer program product may be readily implemented, fully or partially, in software using, for example, object or object-oriented software development environments that provide portable source code that can be used on a variety of computer platforms.
- embodiments of the disclosed method, system, and computer program product can be implemented partially or fully in hardware using, for example, standard logic circuits or a VLSI design.
- Other hardware or software can be used to implement embodiments depending on the speed and/or efficiency requirements of the systems, the particular function, and/or particular software or hardware system, microprocessor, or microcomputer being utilized.
- Embodiments of the method, system, and computer program product can be implemented in hardware and/or software using any known or later developed systems or structures, devices and/or software by those of ordinary skill in the applicable art from the function description provided herein and with a general basic knowledge of the software engineering and computer networking arts.
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US11126662B2 (en) | 2021-09-21 |
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